System and method for forecasting and managing returned merchanidse in retail

Abstract

Systems, methods, and other embodiments that are associated with a computer application configured to execute on a computing device, for providing forecasting and management of returned retail items, are described. In one embodiment, historical retail data associated with a retail item sold at a retail location is read from a data structure associated with the computer application. The historical retail data includes sales data and returns data for the retail item over a plurality of retail periods. Based on the historical retail data, a probability is determined and generated for which a percentage of the retail item sold in one retail period will be returned in at least one subsequent retail period.

What is claimed is: 
     
         1 . A method implemented by a computer application configured to execute on a computing device, wherein the computer application is configured to process retail data in electronic form, the method comprising:
 for a retail item sold at a retail location:   reading, from at least one data structure, historical retail data representing sales data and returns data for the retail item over a plurality of retail periods; and   determining and generating, based on the historical retail data, a probability for which a percentage of the retail item sold in one retail period will be returned in at least one subsequent retail period.   
     
     
         2 . The method of  claim 1 , wherein the determining comprises:
 generating cross-correlation data by performing a cross-correlation operation between the sales data and the returns data for multiple retail period delays between the sales data and the returns data;   generating a return profile, based on the cross-correlation data, over retail period delays of the multiple retail period delays that are positively cross-correlated, where the return profile represents a likelihood that a returned retail item was purchased in a particular previous retail period;   generating average return percentage data, based on the historical retail data, over retail period delays of the multiple retail period delays that are positively cross-correlated; and   generating a final percentage return value, based on the return profile and the average return percentage data, representing the percentage of the retail item sold that is forecast to be returned over the at least one subsequent retail period with probability given by the return profile.   
     
     
         3 . The method of  claim 2 , further comprising repeating the method for a plurality of retail items sold at a plurality of retail locations. 
     
     
         4 . The method of  claim 2 , further comprising determining an order quantity for the retail item based, at least in part, on at least one of the final percentage return value and the return profile. 
     
     
         5 . The method of  claim 1 , wherein the sales data includes a numerical amount of the retail item sold for each retail period of the plurality of retail periods. 
     
     
         6 . The method of  claim 1 , wherein the returns data includes a numerical amount of the retail item returned for each retail period of the plurality of retail periods. 
     
     
         7 . The method of  claim 1 , wherein each retail period of the plurality of retail periods represents one of a day, a week, a month, or a year. 
     
     
         8 . The method of  claim 1 , wherein each retail period delay of the multiple retail period delays represents one of a day, a week, a month, or a year. 
     
     
         9 . The method of  claim 1 , wherein the retail location includes one of a physical store or an on-line store. 
     
     
         10 . A computing system, comprising:
 visual user interface logic configured to facilitate inputting of historical retail data, representing sales data and returns data for a retail item over a plurality of retail periods, into at least one data structure associated with a retail returns management and forecasting application;   correlation logic configured to determine a cross-correlation between the sales data and the returns data for multiple retail period delays between the sales data and the returns data;   profile logic configured to generate a return profile, based on the cross-correlation, over retail period delays of the multiple retail period delays that are positively cross-correlated, where the return profile represents a likelihood that a returned retail item was purchased in a particular previous retail period;   averaging logic configured to generate average return percentage data, based on the historical retail data, over retail period delays of the multiple retail period delays that are positively cross-correlated; and   returns forecasting logic configured to generate a final percentage return value, based on the return profile and the average return percentage data, representing a percentage of the retail item sold that is forecast to be returned during at least one next retail period with probability given by the return profile.   
     
     
         11 . The computing system of  claim 10 , wherein the returns forecasting logic is configured to determine an order quantity for the retail item based, at least in part, on at least one of the final percentage return value and the return profile. 
     
     
         12 . The computing system of  claim 10 , wherein the returns forecasting logic is configured to predict an inventory of the retail item for a future retail period based, at least in part, on at least one of the final percentage return value and the return profile. 
     
     
         13 . The computing system of  claim 10 , further comprising a display screen configured to display and facilitate user interaction with at least a graphical user interface associated with the retail returns management and forecasting application, wherein the visual user interface logic is configured to generate the graphical user interface. 
     
     
         14 . The computing system of  claim 13 , wherein the returns forecasting logic is configured to transform an output data structure, associated with the retail returns management and forecasting application, by populating the output data structure with at least the return profile and the final percentage return value. 
     
     
         15 . The computing system of  claim 14 , wherein the returns forecasting logic is configured to operably interact with the visual user interface logic to facilitate displaying of at least the return profile and the final percentage return value of the output data structure, via the graphical user interface, on the display screen. 
     
     
         16 . The computing system of  claim 10 , further comprising a database device configured to store data structures associated with the retail returns management and forecasting application. 
     
     
         17 . A non-transitory computer-readable medium storing computer-executable instructions that are part of an algorithm that, when executed by a computer, cause the computer to perform a method, wherein the instructions comprise instructions configured for:
 reading historical retail data, representing sales data and returns data associated with a retail item sold at a retail location over a plurality of retail periods, from at least one data structure associated with a retail returns management and forecasting application;   transforming the historical retail data into cross-correlation data, representing a similarity between the sales data and the returns data over multiple retail period delays between the sales data and the returns data;   transforming the cross-correlation data into a return profile over retail period delays of the multiple retail period delays that are positively cross-correlated, where the return profile represents a likelihood that a returned retail item was purchased in a particular previous retail period;   transforming the historical retail data into average return percentage data over retail period delays of the multiple retail period delays that are positively cross-correlated; and   transforming the return profile and the average return percentage data into a final percentage return value representing a percentage of the retail item sold that is forecast to be returned during at least one next retail period with probability given by the return profile.   
     
     
         18 . The non-transitory computer-readable medium of  claim 17 , wherein the instructions further include instructions configured for transforming at least one output data structure, associated with the retail returns management and forecasting application, by populating the at least one output data structure with at least the return profile and the final percentage return value. 
     
     
         19 . The non-transitory computer-readable medium of  claim 17 , wherein the instructions further include instructions configured for transforming an order quantity for the retail item based, at least in part, on at least one of the final percentage return value and the return profile. 
     
     
         20 . The non-transitory computer-readable medium of  claim 17 , wherein the instructions further include instructions configured for repeating the method for a plurality of retail items sold at a plurality of retail locations.